Electrode patch, system, and method for detecting indicator of Parkinson's disease in person

The present application is a § 371 National Phase application based on PCT/EP2019/055002, filed on Feb. 28, 2019, which claims the benefit of European application No. 18159445.8 filed Mar. 1, 2018, the subject matter of each of which is incorporated by reference in their entirety.

The invention relates to measurement and analysis of biosignals, and in particular, to a method for detecting at least one indicator of Parkinson's disease (PD) in biosignals measured from a person.

Parkinson's disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. As the disease progresses, the symptoms become more and more unpredictable. There is no accurate data analysis about the symptoms. It is difficult to form a comprehensive view of the time varying symptoms and to find a correct drug and its dosage, and the scheduling of doses, particular in cases where the disease has already progressed to later stages. It may also be difficult to adjust deep brain stimulation (DBS) settings, and to choose optimal treatment method in each situation.

An object of the present disclosure is to provide a method and an apparatus for implementing the method so as to alleviate the above disadvantages. The object of the disclosure is achieved by a method and an arrangement that are characterized by what is stated in the independent claims. The preferred embodiments of the disclosure are disclosed in the dependent claims.

Detection of indicators of PD may be implemented with an electrode patch and a detection system and method according to the present disclosure.

The electrode patch may be in the form of a self-adhesive, patch or strip into which EMG electrodes have been embedded to a fixed configuration. The electrode patch may have flexible or rigid body, for example. Since the electrodes are in a fixed configuration, their distances and positioning with respect to each other always remain constant. This ensures consistent measurements results, thereby reducing the number of variables.

A detection system and method according to the present disclosure may be used to detect different indicators of PD in the measurement data. A principal component representation may be formed from the EMG and acceleration data. Principal components of the principal component representation may be selected such that it groups the measurement data into meaningful categories representing different indicators of type and/or stage and/or severity of symptoms or treatment response of PD. Magnitudes of the different indicators of PD in the measurement data can be calculated, and an assessment of the condition of the person can be formed on the basis of these magnitudes.

An electrode patch and a detection system according to the present disclosure provide a reliable, computationally cost-efficient tool for providing information that can be used in detecting indicators of PD in a person, in assessing progress of PD in the person, and in determining the efficiency of a treatment/medication for PD in the person.

The method and the electrode patch allow the person to move freely during a measurement period. Thus, the method and electrode patch enable measurement periods that may last several days instead of minutes or few hours. The ability to monitor and analyse continuous measurement data over longer periods of time can have a significant effect on forming a clear view on the symptoms and planning of optimal therapy with regards to drug type, and its dosage, and the dosage scheduling. Continuous and longer monitoring of data may also help in adjusting deep brain stimulation settings, and choosing optimal treatment method in each situation.

The present disclosure describes a flexible or rigid electrode patch and a detection system/method for detecting an indicator of PD in a person. The at least one indicator may be detected in biosignal data, such as EMG and acceleration signal data, of the person. In the context of the present disclosure, biosignal data may represent samples of a biosignal or biosignals measured from the person, for example.

An electrode patch according to the present disclosure may comprise electrodes positioned for measuring the EMG signal from a muscle in upper or lower limb of the person. The positioning of the electrodes may be such that when a detection system according to the present disclosure receives the EMG signal, the detection system is able to detect an indicator of PD in a person. The acceleration signal data may represent samples of measurements measuring motion of a limb of a person. The acceleration data may originate from an acceleration sensor attached to the limb of the person. The type and configuration (sample rate, operating range) of the acceleration sensor may be selected for measuring acceleration data associated muscle activity, and in particular with PD, e.g. tremors.

The detection system may be configured to receive an EMG signal originating from the electrode patch that has been attached to upper or lower limb of the person and an acceleration signal associated with the EMG signal, determine a principal component representation of the EMG signal and the acceleration signal, and determine a magnitude of at least one indicator of PD based on the value of the principal component representation. The condition of the person may then be interpreted on the basis of the determined magnitude of the at least one indicator.

In the context of the present disclosure, the principal component representation is formed by one or more principal components. The principal component representation represents a projection of original signal features that may have correlations between each other into uncorrelated signal features in a feature space formed by orthogonal basis vectors. These uncorrelated signal features are the principal components. In the context of the present disclosure, the principal component representation may be based on at least one feature of the EMG signal and the acceleration signal, for example.

Various features of measurement data (i.e. samples of the EMG signal and the acceleration signal) may be utilized in determining the magnitude of the at least one indicator of PD. These features include statistical features of the signals, such as sample histograms, kurtosis and crossing rate, and spectral-based features, such as Fourier transform, periodogram and wavelets. Further, said features may include parameters based on nonlinear dynamics and interrelation between the EMG and the acceleration data, such as coherence and different types of cross-entropies.

A novel electrode patch according to the present disclosure may be used for the measurement of the EMG. The electrode patch may be disposable or reusable. The electrode patch may be made of a sheet of plastic and/or textile into which electrodes have been embedded, for example. Electrode patch may be flexible or rigid. Electrodes may be wet or dry. The measuring area of the electrodes may be circular or other shaped. The electrodes are preferably arranged to a specific configuration in order to ensure sufficient information content of the EMG signal.

Literature widely recommend using a centre-to-centre distance of 2 cm (0.75 inch) between the measurement electrodes in EMG measurements (see e.g. Hermens H J, Freriks B, Merletti R, Stegeman D, Blok J, Rau G, Disselhorst-Klug C, and Hägg G: European recommendations for surface electromyography. Roessingh Research and Development, ISBN 90-75452-15-2, 1999). However, in order to facilitate receiving electromagnetic signals from deeper inside below skin, a distance wider than the recommended 2 cm may be used in the electrode patch according to the present disclosure. Two measurement electrodes may be positioned parallel to the muscle fibres at a centre-to-centre distance of more than 2 cm to less than 4 cm, for example. A reference electrode may be positioned such that a distance from a centre of the reference electrode to an axis passing through centres of the measurement electrodes is at least the distance between the measurement electrodes. Preferably, the distance between the centres of the measurement electrodes is 2.5-3 cm. Reference electrode is preferably placed on an inactive area with regards to muscles. When this configuration of electrodes is being used together with the detection method/system according to the present disclosure, information content of the EMG signal data can be maximized.

A novel detection system and method according to the present disclosure may be used for the EMG signal data in order to determine the magnitude of the at least one indicator of PD on the basis of the measurement data. The EMG signal data preferably originates from an electrode patch according to the present disclosure. In the system and method, a principal component representation may be formed from the EMG signal data and acceleration signal data associated with the EMG signal data, for example. Features extracted from the measurement data of may be used to form a feature vector. Each feature vector may represent features of measurements of one person, for example.

In order to eliminate possible correlations between the extracted features in the feature vector, the feature vector may then be modelled as a weighted sum of basis vectors, where the basis vectors may have been previously solved as eigenvectors of a sample correlation matrix. The sample correlation matrix may have been formed on the basis of measurement data from a plurality of persons, for example. Principal components representing weights for the weighted sum may be solved for the feature vector as a least squares solution, for example.

The principal components are new, uncorrelated features that represent the measurement data in a feature space formed by the basis vectors. Together, the principal components form a principal component representation. However, in order to reduce complexity of the data, only the most significant features may be selected, and an approximation of the measurement data in a reduced-dimension feature space. For example, the principal components may be selected such that measurement data from persons with PD (or a specific type of PD) cluster together in the feature space. Further, the principal components may also be selected such that the data forms clusters representing different severity/stage of PD or effectiveness of particular treatments, for example. Based on the clusters, simple rules for categorizing measurement results can be formed. Once the rules have been determined, measurement can be reliably categorized by applying the rules to a principal component representation of the EMG and acceleration data.

A measurement arrangement implementing the detection system and method according to the present disclosure may be implemented in various ways. For example, a measurement arrangement for detecting an indicator of PD in a person may comprise a flexible or rigid electrode patch according to the present disclosure, a (wearable) sensor module connected to the electrodes of the electrode patch, and a detection system according to the present disclosure. The sensor module may comprise means for measuring an EMG signal from the electrodes, means for measuring an acceleration signal from the upper or lower limb of the person, and means for transmitting the EMG signal and the acceleration signal to the detection system, for example. A computer, cluster of computer servers, or a computing cloud may be used to implement the detection system/method according to the present disclosure. The detection system may receive the measurement data directly from the sensor module or the measurement data may be relayed via transceiver unit. The transceiver unit may be a wireless communications unit, such as a wireless internet router, for example. A smart phone, tablet computer or other portable computing device with wireless communications capabilities may also be used a transceiver unit.

shows an exemplary embodiment of an electrode patch according to the present disclosure. In, a diagrammatic view of a top side of a self-adhesive electrode patchis shown. The bodyof the electrode patch may be made of a flexible material, such as plastics of textile or their combination. The electrode patchcomprises two measurement electrodesand, and a reference electrodeattached or embedded to the bodysuch that surfaces of the electrodes are exposed on the bottom side of the electrode and form a galvanic connection to the skin of person when the patch is applied. In, the measurement electrodes have essentially circular shapes. The sensing area of the electrode can also be rectangular-shaped if this is more suitable for the manufacturing process. The centres of the measurement electrodesandare positioned at a distance dfrom each other. The distance dis 3 cm in. The reference electrodeis positioned aside of the measurement electrodes (i.e. laterally displaced at a distance dfrom an axis A passing through the centres of the measurement electrodesand). The distance dmay be at least the distance d, preferably at least twice the distance d.

The bodyof the electrode patchmay have a self-adhesive surface on its bottom side and a water resistant coating on its top side. The patchinmay be provided with elongated openings that allow the skin below the patch to breathe. The top side of the patchmay have guide markings that help in positioning the patch correctly. The electrode patch may comprise a peel pad on its side in order to facilitate easy removal of the patch once the measurement has been finished.

also shows a measurement uniton the top side of the electrode patch. The measurement unit is galvanically connected to the electrodesa,b, andby flexible conducting wires. The measurement unitmay be integrated to the electrode patchor it may be detachably connected. For example, the patchmay comprise a connector interface in order to form a galvanic connection between the electrodes,, andand a small, wearable measurement unitmeasuring the EMG of the person. The portable measurement unitmay also comprise acceleration sensor for providing the acceleration data used in the method. The measurement unitmay be battery-powered and may be detachably attached to the electrode patchvia the connector interface. The electrode patch may comprise a dock, a pocket or a pouch into which the measurement unit may be placed during use.

shows another example of an electrode patch according to the present disclosure. The details of the patchare similar in most parts with the patchin. However, a conducting wire forming a connection between the measurement unitand a reference electrodeis arranged on the end of a thin, elongated, and flexible strip. Said stripextends from the bodyof the electrode patch. This allows adjustment of the position of the reference electrodewith respect to the measuring electrodesandof the electrode patch, which may be useful with persons with very large diameter of the limb.

shows an exemplary, simplified flow diagram of determining suitable indicators. In, the procedure comprises an initial step, follows through consecutive steps-, and finally ends at end step.

The procedure starts at stepfrom which the procedure continues to step. In step, raw EMG signals are band-pass filtered, amplified and A/D converted. The band-pass filter may be an analog anti-aliasing filter (Butterworth, band-pass 1-500 Hz), and the A/D conversion may be made with a 14-bit A/D converter, for example. Further, motion of limb is registered by using an accelerometer, e.g. a tri-axial accelerometer (range ±16 g, 14-bit A/D converter).

Next, in step, EMG- and acceleration signals are pre-processed. Possible noise may be removed from measured EMG data by using low-pass or band-pass and/or notch filtering, for example. The noise may be originated from surrounding electrical devices (e.g. a DBS unit) and motion, for example.

In the subsequent step, representative time segments of data are be selected for analysis. Preferably, these segments include muscle activities measured during static and dynamic contractions. In addition, these segments preferably cover different times of day (morning, afternoon, evening and night).

Next, in step, several features are extracted from the EMG and acceleration signals. These features may include at least a sample histogram, parameters describing EMG morphology (e.g. sample kurtosis and crossing rate variable) and parameters based on nonlinear dynamics (e.g. recurrence rate of the EMG signal and sample entropy of the acceleration signal), for example.

In the subsequent step, extracted EMG and acceleration signal features are used to form a feature vector. Each feature vector may then be modelled as a weighted sum of basis vectors, where the basis vectors may have been previously solved as eigenvectors of a sample correlation matrix. The sample correlation matrix may be formed on the basis of gathered person data, for example. The weights (principal components) may be solved for each feature vector of a person as a least squares solution, for example.

In the next step, the solved principal components are used to identify changes in the neuromuscular and motor function of subjects between different times of day, between different DBS settings, or between different treatment methods, for example. Only the most significant principal components with regards to symptom severity, treatment response and type of PD may be chosen for further analysis.

Next, in step, the high-dimensional features formed in stepare projected to lower-dimensional space using the chosen principal components. The chosen principal components are used for calculation of indicator that presents the clinical condition of person. The calculated indicator may be time varying scalar value and it may be presented as a graph, for example. The indicator may also be of higher dimension such as a point in two-dimensional plane, for example. The procedure then ends at step.

shows an example of a measurement arrangement according to the present disclosure. In, electrical activation of a muscle is registered (in the form of an EMG signal) by using an electrode patchaccording to the present disclosure. The electrode patchmay be the same as or similar to the examples of, for example. In addition, limb motion is simultaneously registered inby using an accelerometer. In some embodiments, the accelerometermay be integrated to the measurement unit. Alternatively, the accelerometer may be a separate unit.

In, the electrode patchand the accelerometerprovide an EMG signal and an acceleration signal (e.g. in the form of analog voltage signals) to a measurement unitwhere the signals may be A/D converted. The A/D-converted signal data may be either saved into a memory card of the measurement unit(i.e. off-line mode), or sent to a router device(i.e. online mode) that may be a computer or a smart device, for example. In the offline mode, after a measurement session has ended, the measurement data may be uploaded from the memory card to the router device. In the online mode, the measurement unit may send some or all signal data to the router device already before the end of a measurement session. Preferably, the communication between the measurement unitand the routeris wireless as this allows the person to move more freely. The router devicemay be a wireless communications unit, such as a wireless internet router, for example. A smart phone, tablet computer or other portable computing device with wireless communications capabilities may also be used as the router device.

The signal data provided by the measurement unitto the router devicemay be a raw A/D-converted signal data or it may be preprocessed by the measurement unit. For example, the measurement unit may be configured to perform one or both of stepsandin the embodiment of. The measurement unitinmay support both the offline mode and the online mode, and a user may select which of the two modes to use.

In, the signal data is forward to a cloud-based databasefrom the router device. From the cloud-based database, the signal data is sent to a computing unitthat may be a computer or server containing an analysis program, for example. The analysis program which may be in the form of a software program may be configured to carry out a method according to the present disclosure. Thus, the analysis program may be configured for detecting an indicator of PD in the person. The analysis program may be configured to receive (e.g. from the cloud-based database) an EMG signal originating from an electrode patch attached to a limb of the person and an acceleration signal associated with the EMG signal, determine a principal component representation of the EMG signal and the acceleration signal, wherein the principal component representation represents a projection of at least one feature of the EMG signal and the acceleration signal into a feature space formed by orthogonal basis vectors, and determine a magnitude of the indicator of PD based on the principal component representation. For example, the analysis program may implement the stepstoof the example ofin order to detect an indicator of Parkinson's disease.

Once the analysis program in computing unithas been executed, the analysis results may be sent from the computing unitback to the cloud-based database, from where they can be accessed by the doctor who is either in charge of the person's treatment or asked to give his/her statement on results, for example.

The above discussed measurement system may be used as follows, for example.

First, the skin of the person over a muscle of interest is preferably shaved (if required) and rubbed with alcohol. An electrode patch according to the present disclosure may then be placed on the skin and according to the present disclosure may be attached to the electrode patch. A person in charge of the measurement session may check the quality of EMG signals, and start a continuous measurement of EMG and motion which may last for several days, for example. During the measurement, the person is free to move and do his/her daily activities.

The system collects EMG- and motion-based data and either saves them to the measurement unit or transfers it to a cloud-based database. The measured data is then analyzed, and the analysis results are provided to a doctor, who is in charge of the person's treatment. The doctor makes the decision on whether or not to use the results as help in doing the diagnosis or adjusting treatment.

The measurement and analysis according to the present disclosure may be performed for Parkinson patients with DBS therapy, for example. The analysis results may be used as help for adjusting the DBS settings. By performing the measurement before and after the adjustment of DBS settings, the outcome of DBS setting changes can be evaluated. By performing the measurement before and after DBS surgery, the outcome of DBS surgery can be evaluated.

The measurement and analysis according to the present disclosure may also be performed for Parkinson patients with drug therapy. Analysis results describing the time-varying symptoms may be used as an aid for adjusting the drug therapy such as for choosing optimal drug types, for adjusting drug dosages, and for scheduling of drug doses. The analysis results may also be used as help in screening the need for other type of therapy such as the need for DBS therapy.

The measurement and analysis according to the present disclosure may also be performed for Parkinson patients that participate in a clinical trial of drug development. Analysis results may help the drug developers in comparing different drugs and/or evaluating drug efficacy.

The measurement and analysis according to the present disclosure may also be performed for during a surgical operation such as an implantation of neurostimulation system. The measurement system may collect EMG- and motion-based signals continuously and send them to a routing device such as a personal computer or smart device. The measurement data and analysis results are shown online on the personal computer or smart device. The doctor may use the measurement data and analysis results as help for adjusting stimulation electrode placement, configurations and stimulation settings during surgery.

It is obvious to a person skilled in the art that the electrode patch and the detection method/system can be implemented in various ways. The invention and its embodiments are not limited to the examples described above but may vary within the scope of the claims.